Photometric Classification of Early-time Supernova Light Curves with SCONE

Abstract In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe (r < 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package 1 1 github.com/helenqu/scone located at github.com/helenqu/scone (Qu 2021).

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The analysis of VERITAS muon images using convolutional neural networks

Proceedings of the International Astronomical Union ◽

10.1017/s1743921316012734 ◽

2016 ◽

Vol 12 (S325) ◽

pp. 173-179 ◽

Cited By ~ 5

Author(s):

Qi Feng ◽

Tony T. Y. Lin ◽

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Gamma Ray ◽

Cosmic Ray ◽

Classification Problem ◽

Machine Learning Algorithms ◽

Cherenkov Telescopes ◽

Small Set ◽

Regression Problems

AbstractImaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare gamma-ray photons, buried in the background of charged cosmic-ray (CR) particles, the flux of which is several orders of magnitude greater. The ability to separate gamma rays from CR particles is important, as it is directly related to the sensitivity of the instrument. This gamma-ray/CR-particle classification problem in IACT data analysis can be treated with the rapidly-advancing machine learning algorithms, which have the potential to outperform the traditional box-cut methods on image parameters. We present preliminary results of a precise classification of a small set of muon events using a convolutional neural networks model with the raw images as input features. We also show the possibility of using the convolutional neural networks model for regression problems, such as the radius and brightness measurement of muon events, which can be used to calibrate the throughput efficiency of IACTs.

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Video Stream Gender Classification Using Shallow CNN

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421550016 ◽

2020 ◽

pp. 2155001

Author(s):

Oleksii Gorokhovatskyi ◽

Olena Peredrii

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Classification Problem ◽

Video Stream ◽

Gender Classification ◽

Single Frame ◽

Static Image ◽

Image Datasets

This paper describes the investigation results about the usage of shallow (limited by few layers only) convolutional neural networks (CNNs) to solve the video-based gender classification problem. Different architectures of shallow CNN are proposed, trained and tested using balanced and unbalanced static image datasets. The influence of diverse voting over confidences methods, applied for frame-by-frame gender classification of the video stream, is investigated for possible enhancement of the classification accuracy. The possibility of the grouping of shallow networks into ensembles is investigated; it has been shown that the accuracy may be more improved with the further voting of separate shallow CNN classification results inside an ensemble over a single frame or different ones.

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SUPERVISED DETECTION OF BOMB CRATERS IN HISTORICAL AERIAL IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w16-67-2019 ◽

2019 ◽

Vol XLII-2/W16 ◽

pp. 67-74 ◽

Cited By ~ 1

Author(s):

D. Clermont ◽

C. Kruse ◽

F. Rottensteiner ◽

C. Heipke

Keyword(s):

Neural Networks ◽

World War Ii ◽

Convolutional Neural Networks ◽

Classification Problem ◽

Aerial Images ◽

Explosion Hazard ◽

World War ◽

Combining Data ◽

The Given

<p><strong>Abstract.</strong> The aftermath of the air strikes during World War II is still present today. Numerous bombs dropped by planes did not explode, still exist in the ground and pose a considerable explosion hazard. Tracking down these duds can be tackled by detecting bomb craters. The existence of a dud can be inferred from the existence of a crater. This work proposes a method for the automatic detection of bomb craters in aerial wartime images. First of all, crater candidates are extracted from an image using a blob detector. Based on given crater references, for every candidate it is checked whether it, in fact, represents a crater or not. Candidates from various aerial images are used to train, validate and test Convolutional Neural Networks (CNNs) in the context of a two-class classification problem. A loss function (controlling what the CNNs are learning) is adapted to the given task. The trained CNNs are then used for the classification of crater candidates. Our work focuses on the classification of crater candidates and we investigate if combining data from related domains is beneficial for the classification. We achieve a F1-score of up to 65.4% when classifying crater candidates with a realistic class distribution.</p>

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Dog Breed Identification with Fine tuning of Pre-trained models

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1464.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 3677-3680

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Classification Problem ◽

Training Data ◽

Fine Tuning ◽

Dog Breed ◽

Classification Of Images ◽

Multiclass Classifier

Dog Breed identification is a specific application of Convolutional Neural Networks. Though the classification of Images by Convolutional Neural Network serves to be efficient method, still it has few drawbacks. Convolutional Neural Networks requires a large amount of images as training data and basic time for training the data and to achieve higher accuracy on the classification. To overcome this substantial time we use Transfer Learning. In computer vision, transfer learning refers to the use of a pre-trained models to train the CNN. By Transfer learning, a pre-trained model is trained to provide solution to classification problem which is similar to the classification problem we have. In this project we are using various pre-trained models like VGG16, Xception, InceptionV3 to train over 1400 images covering 120 breeds out of which 16 breeds of dogs were used as classes for training and obtain bottleneck features from these pre-trained models. Finally, Logistic Regression a multiclass classifier is used to identify the breed of the dog from the images and obtained 91%, 94%,95% validation accuracy for these different pre-trained models VGG16, Xception, InceptionV3.

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CNN-based Classification of Degraded Images

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-028 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 28-1-28-7 ◽

Cited By ~ 1

Author(s):

Kazuki Endo ◽

Masayuki Tanaka ◽

Masatoshi Okutomi

Keyword(s):

Neural Networks ◽

Image Restoration ◽

Image Classification ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Alternative Approach ◽

Degraded Image ◽

Degraded Images ◽

Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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